Puns, as a unique form of linguistic creativity, present significant challenges in cross-lingual translation, particularly between linguistically distant languages like Chinese and English, where it’s often considered a “mission impossible”. We introduce Pun2Pun, a novel benchmark for quantitatively evaluating pun translation between Chinese and English while preserving both linguistic mechanisms and humorous effects. We propose the adaptation of Constant-Variable Optimization (CVO) Model for translation strategy and concomitant Overlap (Ovl) metric for translation quality assessment. Our approach provides a robust quantitative evaluation framework to assess models’ complex linguistic and cultural reasoning capabilities in pun translation. Through extensive experiments on both textual and visual puns, we demonstrate that our translation strategy model significantly improves performance, particularly for better-performing models. Our findings reveal exciting potentials and current limitations of LLMs in preserving sophisticated humor across linguistic and cultural boundaries.

In the rapidly evolving field of natural language processing, dialogue systems primarily employ a single-step dialogue paradigm. Although this paradigm is commonly adopted, it lacks the depth and fluidity of human interactions and does not appear natural. We introduce a novel **Step**-by-Step Dialogue Paradigm (Stephanie), designed to mimic the ongoing dynamic nature of human conversations. By employing a dual learning strategy and a further-split post-editing method, we generated and utilized a high-quality step-by-step dialogue dataset to fine-tune existing large language models, enabling them to perform step-by-step dialogues. We thoroughly present Stephanie. Tailored automatic and human evaluations are conducted to assess its effectiveness compared to the traditional single-step dialogue paradigm. We will release code, Stephanie datasets, and Stephanie LLMs to facilitate the future of chatbot eras.

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.